Receding horizon flight control for trajectory tracking of autonomous aerial vehicles

Summary This paper focuses on the longitudinal control of an Airbus passenger aircraft in the presence of elevator jamming faults. In particular, in this paper, we address permanent and temporary actuator jamming faults using a novel reconfigurable fault‐tolerant predictive control design. Due to their different consequences on the available control authority and fault duration, the above 2 actuator jamming faults need to be distinguished so that appropriate control reconfigurations can be adopted accordingly. Their similarity in symptoms, however, prevents an effective discrimination of the root cause of the jamming when using only a passive fault‐diagnosis approach. Hence, we propose the use of model predictive control (MPC) as a fault‐tolerant controller to actively help the fault‐detection (FD) unit discriminate between a permanent and a temporary jamming fault, while ensuring the performance of the aircraft. The MPC controller and the FD unit closely interact during the detection and diagnosis phases. In particular, every time a fault is detected, the FD module commands the MPC controller to perform a predefined sequence of reconfigurations to diagnose the root cause of the fault. An artificial reference signal that accounts for changes in the actuator operative ranges is used to guide the system through this sequence of reconfigurations. Our strategy is demonstrated on an Airbus passenger aircraft simulator.

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“…Nevertheless, the approach can potentially be extended to linear parameter‐varying or linear time‐varying models. ()…”

Section: Ftc Architecturementioning

confidence: 99%

“…Nevertheless, the approach can potentially be extended to linear parameter-varying or linear time-varying models. [38][39][40][41] In the remainder of this paper, we consider the following assumption. Assumption 1.…”

Section: Model Predictive Controllermentioning

confidence: 99%

Fault‐tolerant reference generation for model predictive control with active diagnosis of elevator jamming faults

Ferranti

Wan

Keviczky

2018

Intl J Robust & Nonlinear

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“…Solving a nonlinear optimisation problem with constraints is computationally intensive, which is the main obstacle that blocks the real-time application of MPC to nonlinear systems. To reduce the computational burden of online optimisation, the differential flatness property has been exploited in many trajectory planning algorithms which are actually MPC problems (Berry, Howitt, Gu, & Postlethwaite, 2011;Cowling, Yakimenko, Whidborne, & Cooke, 2010;Faulwasser, Hagenmeyer, & Findeisen, 2014;Flores & Milam, 2006;Mahadevan, Agrawal, & Doyle, 2001;Prodan et al, 2013;Suryawan, De Don, & Seron, 2012). Loosely speaking, the differential flatness means that the flat system can be completely characterised by flat outputs and their higher order derivatives (Fliess, Lvine, Martin, & Rouchon, 1995).…”

Section: Introductionmentioning

confidence: 99%

Constrained anti-disturbance control for a quadrotor based on differential flatness

Lü

Liu

Guo

et al. 2016

International Journal of Systems Science

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The classical control design based on linearised model is widely used in practice even to those inherently nonlinear systems. Although linear design techniques are relatively mature and enjoy the simple structure in implementations, they can be prone to misbehaviour and failure when the system state is far away from the operating point. To avoid the drawbacks and exploit the advantages of linear design methods while tackling the system nonlinearity, a hybrid control structure is developed in this paper. First, the model predictive control is used to impose states and inputs constraints on the linearised model, which makes the linearisation satisfy the small-perturbation requirement and reduces the bound of linearisation error. On the other hand, a combination of disturbance observer based control and H∞ control, called composite hierarchical anti-disturbance control, is constructed for the linear model to provide robustness against multiple disturbances. The constrained reference states and inputs generated by the outer-loop model predictive controller are asymptotically tracked by the inner-loop composite anti-disturbance controller. To demonstrate the performance of the proposed framework, a case study on quadrotor is conducted.

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“…Yang et al presented an adaptive nonlinear MPC controller for the path following of a fixed-wing UAV with the variable control horizon depending upon the path curvature profile, and the adaptive scheme was verified by simulation comparing with a conventional fixed-horizon MPC. 8 Prodan et al 22 applied the MPC for trajectory tracking of UAV, where the core MPC algorithm was run in MATLAB (Product of the MathWorks, Inc) off-board to generate the guidance law which was sent to the onboard avionic through communication link. The above scenarios indicate that the MPC method is robust for path following, but it needs much more numerical calculation which makes it difficult to be applied in the low-cost onboard flight control system.…”

Section: Introductionmentioning

confidence: 99%

Combined of Lyapunov-stable and active disturbance rejection control for the path following of a small unmanned aerial vehicle

Yang

Liang

Wang³

et al. 2017

International Journal of Advanced Robotic Systems

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This article proposes a composite path following controller that allows the small fixed-wing unmanned aerial vehicle to follow a predefined path. Assuming that the vehicle is equipped with an autopilot for altitude and airspeed maintained well, the controller design adopts the hierarchical control structure. With the inner-loop controller design based on the notion of active disturbance rejection control which will respond to the desired roll angle command, the core part of the outerloop controller is designed based on Lyapunov stability theorem to generate the desired course rate for the straight-line paths. The bank to turn maneuver is used to transform the desired course rate to the desired roll angle command. Both the hardware-in-the-loop simulation in the X-Plane simulator and actual experimental flight tests have been successfully achieved, which verified the effectiveness of the proposed method.

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Receding horizon flight control for trajectory tracking of autonomous aerial vehicles

Cited by 65 publications

References 39 publications

Fault‐tolerant reference generation for model predictive control with active diagnosis of elevator jamming faults

Fault‐tolerant reference generation for model predictive control with active diagnosis of elevator jamming faults

Constrained anti-disturbance control for a quadrotor based on differential flatness

Combined of Lyapunov-stable and active disturbance rejection control for the path following of a small unmanned aerial vehicle

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